The paper entitled "Limits of Risk Predictability in a Cascading Alternating Renewal Process Model," by Xin Lin, Alaa Moussawi, Gyorgy Korniss, Jonathan Z. Bakdash, and Boleslaw K. Szymanski was published by Scientific Reports7:6699, 2017. Most risk analysis models systematically underestimate the probability and impact of catastrophic events (e.g., economic crises, natural disasters, and terrorism) by not taking into account interconnectivity and interdependence of risks. To address this weakness, the paper propose the Cascading Alternating Renewal Process (CARP) to forecast networked risks. However, assessments of the model’s prediction precision are limited by lack of sufficient ground truth data. The paper establishes prediction precision as a function of input data size by using alternative long ground truth data generated by simulations of the CARP model with known parameters. The approach is simulated on a model of fires in artificial cities assembled from basic city blocks with diverse housing. The results confirm that parameter recovery variance exhibits power law decay as a function of the length of available ground truth data. The paper demonstrates limits of prediction using another dataset with dependencies: real-world prediction precision for the global risk model based on the World Economic Forum Global Risk Report. Thus, the paper demonstrates that the CARP model is an efficient method for predicting catastrophic cascading events with potential applications to emerging local and global interconnected risks.